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Data Vault Mappings to Dimensional Model Using Schema Matching

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
OtsikkoResearch and Practical Issues of Enterprise Information Systems - 13th IFIP WG 8.9 International Conference, CONFENIS 2019, Proceedings
ToimittajatPetr Doucek, Josef Basl, Antonin Pavlicek, A Min Tjoa, Katrin Detter, Maria Raffai
KustantajaSpringer
Sivut55-64
Sivumäärä10
ISBN (painettu)9783030376314
DOI - pysyväislinkit
TilaJulkaistu - 1 tammikuuta 2019
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIFIP WG 8.9 Working Conference on Research and Practical Issues of Enterprise Information Systems - Prague, Tshekki
Kesto: 16 joulukuuta 201917 joulukuuta 2019

Julkaisusarja

NimiLecture Notes in Business Information Processing
Vuosikerta375
ISSN (painettu)1865-1348
ISSN (elektroninen)1865-1356

Conference

ConferenceIFIP WG 8.9 Working Conference on Research and Practical Issues of Enterprise Information Systems
MaaTshekki
KaupunkiPrague
Ajanjakso16/12/1917/12/19

Tiivistelmä

In data warehousing, business driven development defines data requirements to fulfill reporting needs. A data warehouse stores current and historical data in one single place. Data warehouse architecture consists of several layers and each has its own purpose. A staging layer is a data storage area to assists data loadings, a data vault modelled layer is the persistent storage that integrates data and stores the history, whereas publish layer presents data using a vocabulary that is familiar to the information users. By following the process which is driven by business requirements and starts with publish layer structure, this creates a situation where manual work requires a specialist, who knows the data vault model. Our goal is to reduce the number of entities that can be selected in a transformation so that the individual developer does not need to know the whole solution, but can focus on a subset of entities (partial schema). In this paper, we present two different schema matchers, one based on attribute names, and another based on data flow mapping information. Schema matching based on data flow mappings is a novel addition to current schema matching literature. Through the example of Northwind, we show how these two different matchers affect the formation of a partial schema for transformation source entities. Based on our experiment with Northwind we conclude that combining schema matching algorithms produces correct entities in the partial schema.